1. Field of the Invention
The present invention relates generally to organizing color values, and more particularly, to a method and system for organizing color values using artificial intelligence.
2. Description of the Prior Art
Products today are offered to consumers in a wide variety of colors. Consumer products may be colored by means of colorants, dye or paint. Color matching is required in a variety of areas, including textiles, plastics, various synthetic materials, prosthetics, dental applications, and paint applications, due to the many variations in color, due to the wide variations in shades and hues of any given color and color variations in an article. The actual color produced in a given article may vary due to a number of factors. For example, textile colors vary according to fiber composition. Colorants for plastic vary according to the plastic composition. Painted articles vary in color depending on any number of factors, such as paint composition, variations in the paint application process, including application method, film thickness, drying technique and number of layers. An important application for color matching is in the area of automotive color matching. Frequent uses for color matching in automotive paint occur in matching the same color from different batches or matching similar colors from different manufacturers. Additionally, there is a requirement for color matching refinish paint to an OEM (original equipment manufacture) color when a vehicle body panels are damaged and require repainting.
A paint manufacturer supplies one or more paint formulations for the original paint color to refinish paint shops. By supplying a plurality of formulations or variants for a particular color, the paint manufacturer accounts for those factors which affect the actual color. Matching of dyes or colorants for other applications is also done through formulations for a particular color. Typically, the formulations for a particular color are distributed on paper, microfiche, and/or compact disks (CD). A color tool, composed of swatches of the variants for each color may also be produced and delivered to each customer. The customer must select a formulation most closely matching the existing color of the article. This is typically done visually, i.e., by comparing swatches of paint or color to the part or in the case of paint, spraying a test piece with each formulation.
Different formulations are derived from actual data gathered by inspectors at various locations, e.g., the textile, plastic or automobile manufacturer or vehicle distribution point. The inspectors take color measurement readings from articles of a particular color. These readings are used to develop color solutions, i.e., different formulations for the same color.
There are several disadvantages to the present method of color matching. Conventional color laboratories that use human analysis to determine color matching require significant numbers of people, equipment and materials for identifying pigments and locating a close match from a database. In some cases, an existing formula may provide a close match. In other cases, the formula must be adjusted, mixed, applied and compared to a standard. These steps are repeated until a suitably close match is found. In other cases, no match is found and a formula must be developed from scratch. Correction of the formula requires a highly skilled technician proficient in the interaction of light with several different pigments.
Moreover, traditional computer software that assists a technician has several disadvantages. Traditional computer software has not proven to be very effective on colors containing “effect pigments.” This software is typically based on a physical model of the interaction between illuminating light and the colorant or coating. These models involve complex physics and do not account for all aspects of the phenomena. A traditional approach is to use a model based on the work of Kubleka-Munk or modifications thereof. The model is difficult to employ with data obtained from multi-angle color measuring devices. One particular difficulty is handling specular reflection that occurs near the gloss angle. Another deficiency of the Kubleka-Munk based models is that only binary or ternary pigment mixtures are used to obtain the constants of the model. Thus, the model may not properly account for the complexities of the multiple interactions prevalent in many paint, dye or colorant recipes.